The present invention relates generally to the field of causal graph construction, and more particularly to an interactive tool for causal graph construction.
In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs are graphical models used to encode assumptions about a data-generating process. Causal graphs can be used for communication and for inference. As communication devices, causal graphs provide formal and transparent representation of the causal assumptions that researchers may wish to convey and defend. As inference tools, causal graphs enable researchers to estimate effect sizes from non-experimental data, derive testable implications of the assumptions encoded, test for external validity, and manage missing data and selection bias.
The two basic units of which these graphs are constructed are vertices (i.e. nodes) and edges. Each edge has two vertices to which it is attached, called its endpoints. Edges may be directed or undirected, directed edges are also called arcs or arrows and undirected edges are also called lines. For example, an arrow (x, y) is considered to be directed from x to y; y is called the head and x is called the tail of the arrow; y is said to be a direct successor of x and x is said to be a direct predecessor of y. If a path leads from x to y, then y is said to be a successor of x and reachable from x, and x is said to be a predecessor of y. An undirected edge, or a line, has no direction and therefore can be bidirectional.